In [ ]:
import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt
In [ ]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [ ]:
stocks = px.data.stocks()
stocks.head()
Out[ ]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [ ]:
# YOUR CODE HERE
x = stocks['date']
y = stocks['GOOG']
fig, ax = plt.subplots(figsize = (15, 10))
ax.plot(x,y)
xticks = range(0, stocks.shape[0], 14)
ax.set_xticks(xticks)
ax.set_title('Google stock')
ax.set_xlabel('date')
ax.set_ylabel('stock value')
plt.show()

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [ ]:
# YOUR CODE HERE
x = stocks['date']
fig, ax = plt.subplots(figsize = (15, 10))

ax.plot(x, stocks['GOOG'], label = 'GOOG', color = 'tab:blue')
ax.plot(x, stocks['AAPL'], label = 'AAPL', color = 'tab:orange')
ax.plot(x, stocks['AMZN'], label = 'AMZN', color = 'g')
ax.plot(x, stocks['FB'], label = 'FB', color = 'r')
ax.plot(x, stocks['NFLX'], label = 'NFLX', color = 'tab:purple')
ax.plot(x, stocks['MSFT'], label = 'MSFT', color = 'tab:brown')

xticks = range(0, stocks.shape[0], 14)
ax.set_xticks(xticks)
ax.set_title('Stocks')
ax.set_xlabel('date')
ax.set_ylabel('stock value')
plt.legend()
plt.show()

Seaborn¶

First, load the tips dataset

In [ ]:
tips = sns.load_dataset('tips')
tips.head()
Out[ ]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [ ]:
# YOUR CODE HERE
# Question: Are there differences between male and female when it comes to giving tips?

# Plot that supports drawing answers:
g = sns.FacetGrid(tips, col='smoker', row='time', hue='sex')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
plt.show()

Plotly Express¶

Question 4:¶

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [ ]:
# YOUR CODE HERE 

fig = px.line(stocks, x='date', y=['GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT'])
fig.show()

The tips dataset¶

In [ ]:
# YOUR CODE HERE
fig = px.scatter(tips, x='total_bill', y='tip', color='sex', facet_col='smoker', facet_row='time', width=1000, height=500)
fig.show()

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [ ]:
#load data
df = px.data.gapminder()
df.head()
Out[ ]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [ ]:
# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_new, x="pop", y=df_2007_new.index, orientation='h', 
            color=['orange', 'purple', 'red', 'blue', 'green'], text='pop')
fig.update_yaxes(categoryorder='total ascending')
fig.update_traces(showlegend=False)
fig.update_traces(textposition='outside')
fig.update_traces(texttemplate='%{text:.2s}')
fig.show()